diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index bda578cc5..31ae46994 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -59,15 +59,14 @@ def samples_to_images_tensor(sample, approximation=None, model=None):
 
     return x_sample
 
-
+lp_stream = torch.cuda.Stream()
 def single_sample_to_image(sample, approximation=None):
-    x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
-
-    x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
-    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-    x_sample = x_sample.astype(np.uint8)
-
-    return Image.fromarray(x_sample)
+    with torch.cuda.stream(lp_stream):
+        x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
+        x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
+        x_sample = 255. * x_sample.permute(1, 2, 0)
+        x_sample = x_sample.to(device='cpu', dtype=torch.uint8, non_blocking=True)
+        return x_sample
 
 
 def decode_first_stage(model, x):
@@ -81,7 +80,9 @@ def sample_to_image(samples, index=0, approximation=None):
 
 
 def samples_to_image_grid(samples, approximation=None):
-    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+    sample_tensors = [single_sample_to_image(sample, approximation) for sample in samples]
+    lp_stream.synchronize()
+    return images.image_grid([Image.fromarray(sample.numpy()) for sample in sample_tensors])
 
 
 def images_tensor_to_samples(image, approximation=None, model=None):